Free US drug-price benchmarks from federal data (CMS NADAC): a benchmark, not a price anyone owes.
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or overlap. The tool's purpose is clearly defined and distinct.
With a single tool, naming consistency is trivial. The name 'drug_price' is descriptive and follows a simple noun-based convention, which is internally consistent.
A single tool is on the low end of the acceptable range. While the tool is substantial and not trivial, the server may feel thin for a broader drug-pricing domain.
The tool provides a comprehensive set of drug-pricing data including NADAC, cash-price estimates, generics, and Medicare negotiated prices. It handles ambiguity through candidate responses and disambiguation calls, covering the core lookup workflow.
Look up what US pharmacies actually pay to acquire a drug (CMS NADAC benchmark), a fair cash-price estimate range, FDA generic equivalents, and Medicare negotiated prices where applicable. Returns match, candidates (disambiguate and call again), or no_match.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Drug name with optional strength/form, e.g. "atorvastatin 20 mg" or "eliquis 5 mg tablet". Generic (ingredient) names match best. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool can return multiple candidates or no match, and notes that prices are provided 'where applicable', setting expectations for coverage. It also clearly indicates the nature of the data (pharmacy acquisition benchmark). It does not mention permissions or rate limits, but for a read-only lookup the key behavior is addressed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and the second sentence efficiently encapsulates output behavior. Every clause contributes information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter lookup tool without an output schema, the description provides sufficient context: it enumerates the types of data returned, the possible outcomes, and the disambiguation workflow. It does not detail the exact structure of price entries, but the described behavior is complete for practical use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already fully describes the 'query' parameter with examples. The description adds value by emphasizing that generic ingredient names match best, which is a useful heuristic not present in the schema. This goes beyond the baseline for 100% schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('look up') and clearly specifies the resources: US pharmacy acquisition costs (NADAC), fair cash-price estimates, generic equivalents, and Medicare negotiated prices. It precisely distinguishes this tool's scope from other potential price-related tools, even in the absence of siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains the output types (match, candidates, no_match) and instructs the agent to call again when disambiguation is needed. However, it does not explicitly state when to use this tool versus alternatives, though no alternatives are listed. The context of usage is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
drug_priceLive US drug acquisition costs (CMS NADAC) for AI assistants. Free, no auth, weekly data.
US drug price benchmarks — what a pharmacy PAYS and what Medicare PAYS OUT.
US Rx prices by pharmacy + free discount card; also US equivalents of foreign brands, 12 languages
CMS quality ratings, payer-negotiated prices, and clinician data for 41K+ US healthcare facilities.
Add one secure layer between your agents and this server.
